Overview

Dataset statistics

Number of variables29
Number of observations3096313
Missing cells12139874
Missing cells (%)13.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory740.9 MiB
Average record size in memory250.9 B

Variable types

Numeric9
Categorical19
DateTime1

Alerts

i94yr has constant value "2016.0"Constant
i94mon has constant value "4.0"Constant
count has constant value "1.0"Constant
matflag has constant value "M"Constant
updated_at has constant value "2022-12-10 21:24:30.135479"Constant
i94port has a high cardinality: 299 distinct valuesHigh cardinality
i94addr has a high cardinality: 457 distinct valuesHigh cardinality
visapost has a high cardinality: 530 distinct valuesHigh cardinality
occup has a high cardinality: 111 distinct valuesHigh cardinality
dtaddto has a high cardinality: 777 distinct valuesHigh cardinality
insnum has a high cardinality: 1913 distinct valuesHigh cardinality
airline has a high cardinality: 534 distinct valuesHigh cardinality
fltno has a high cardinality: 7152 distinct valuesHigh cardinality
cicid is highly overall correlated with arrdate and 1 other fieldsHigh correlation
i94cit is highly overall correlated with i94res and 2 other fieldsHigh correlation
i94res is highly overall correlated with i94cit and 2 other fieldsHigh correlation
arrdate is highly overall correlated with cicidHigh correlation
depdate is highly overall correlated with arrdateHigh correlation
i94bir is highly overall correlated with biryearHigh correlation
biryear is highly overall correlated with i94birHigh correlation
entdepa is highly overall correlated with i94mode and 3 other fieldsHigh correlation
visatype is highly overall correlated with i94cit and 5 other fieldsHigh correlation
entdepu is highly overall correlated with entdepdHigh correlation
i94visa is highly overall correlated with visatypeHigh correlation
i94mode is highly overall correlated with entdepa and 1 other fieldsHigh correlation
entdepd is highly overall correlated with i94mode and 3 other fieldsHigh correlation
admnum is highly overall correlated with cicid and 5 other fieldsHigh correlation
i94addr has 152592 (4.9%) missing valuesMissing
depdate has 142457 (4.6%) missing valuesMissing
visapost has 1881250 (60.8%) missing valuesMissing
occup has 3088187 (99.7%) missing valuesMissing
entdepd has 138429 (4.5%) missing valuesMissing
entdepu has 3095921 (> 99.9%) missing valuesMissing
matflag has 138429 (4.5%) missing valuesMissing
gender has 414269 (13.4%) missing valuesMissing
insnum has 2982605 (96.3%) missing valuesMissing
airline has 83627 (2.7%) missing valuesMissing
depdate is highly skewed (γ1 = 301.4912075)Skewed
dtadfile is highly skewed (γ1 = -62.60338146)Skewed
cicid has unique valuesUnique

Reproduction

Analysis started2022-12-10 13:31:27.586910
Analysis finished2022-12-10 13:39:31.014372
Duration8 minutes and 3.43 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

cicid
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct3096313
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3078651.9
Minimum6
Maximum6102785
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.2 MiB
2022-12-10T21:39:31.149303image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile280126.6
Q11577790
median3103507
Q34654341
95-th percentile5770285.4
Maximum6102785
Range6102779
Interquartile range (IQR)3076551

Descriptive statistics

Standard deviation1763278.1
Coefficient of variation (CV)0.57274358
Kurtosis-1.186781
Mean3078651.9
Median Absolute Deviation (MAD)1528755
Skewness-0.018431938
Sum9.5324698 × 1012
Variance3.1091497 × 1012
MonotonicityNot monotonic
2022-12-10T21:39:31.401093image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4904480 1
 
< 0.1%
1540172 1
 
< 0.1%
1540156 1
 
< 0.1%
1540157 1
 
< 0.1%
1540158 1
 
< 0.1%
1540159 1
 
< 0.1%
1540160 1
 
< 0.1%
1540161 1
 
< 0.1%
1540168 1
 
< 0.1%
1540169 1
 
< 0.1%
Other values (3096303) 3096303
> 99.9%
ValueCountFrequency (%)
6 1
< 0.1%
7 1
< 0.1%
15 1
< 0.1%
16 1
< 0.1%
17 1
< 0.1%
18 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
21 1
< 0.1%
22 1
< 0.1%
ValueCountFrequency (%)
6102785 1
< 0.1%
6101166 1
< 0.1%
6101165 1
< 0.1%
6101164 1
< 0.1%
6101163 1
< 0.1%
6101162 1
< 0.1%
6101161 1
< 0.1%
6101160 1
< 0.1%
6101159 1
< 0.1%
6101158 1
< 0.1%

i94yr
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 MiB
2016.0
3096313 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters18577878
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016.0
2nd row2016.0
3rd row2016.0
4th row2016.0
5th row2016.0

Common Values

ValueCountFrequency (%)
2016.0 3096313
100.0%

Length

2022-12-10T21:39:31.585794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-10T21:39:31.760700image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2016.0 3096313
100.0%

Most occurring characters

ValueCountFrequency (%)
0 6192626
33.3%
2 3096313
16.7%
1 3096313
16.7%
6 3096313
16.7%
. 3096313
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15481565
83.3%
Other Punctuation 3096313
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6192626
40.0%
2 3096313
20.0%
1 3096313
20.0%
6 3096313
20.0%
Other Punctuation
ValueCountFrequency (%)
. 3096313
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 18577878
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6192626
33.3%
2 3096313
16.7%
1 3096313
16.7%
6 3096313
16.7%
. 3096313
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18577878
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6192626
33.3%
2 3096313
16.7%
1 3096313
16.7%
6 3096313
16.7%
. 3096313
16.7%

i94mon
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 MiB
4.0
3096313 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9288939
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row4.0
4th row4.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 3096313
100.0%

Length

2022-12-10T21:39:31.889064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-10T21:39:32.064332image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
4.0 3096313
100.0%

Most occurring characters

ValueCountFrequency (%)
4 3096313
33.3%
. 3096313
33.3%
0 3096313
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6192626
66.7%
Other Punctuation 3096313
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 3096313
50.0%
0 3096313
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3096313
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9288939
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 3096313
33.3%
. 3096313
33.3%
0 3096313
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9288939
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 3096313
33.3%
. 3096313
33.3%
0 3096313
33.3%

i94cit
Real number (ℝ)

Distinct243
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean304.90693
Minimum101
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.2 MiB
2022-12-10T21:39:32.271893image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile111
Q1135
median213
Q3512
95-th percentile691
Maximum999
Range898
Interquartile range (IQR)377

Descriptive statistics

Standard deviation210.02689
Coefficient of variation (CV)0.68882293
Kurtosis-0.80809704
Mean304.90693
Median Absolute Deviation (MAD)84
Skewness0.87415268
Sum9.4408730 × 108
Variance44111.294
MonotonicityNot monotonic
2022-12-10T21:39:32.542463image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135 360157
 
11.6%
209 206873
 
6.7%
245 191425
 
6.2%
111 188766
 
6.1%
582 175781
 
5.7%
148 157806
 
5.1%
254 137735
 
4.4%
689 129833
 
4.2%
213 110691
 
3.6%
438 109884
 
3.5%
Other values (233) 1327362
42.9%
ValueCountFrequency (%)
101 828
 
< 0.1%
102 82
 
< 0.1%
103 16136
 
0.5%
104 20359
 
0.7%
105 2571
 
0.1%
107 17027
 
0.5%
108 24797
 
0.8%
109 2108
 
0.1%
110 11954
 
0.4%
111 188766
6.1%
ValueCountFrequency (%)
999 894
< 0.1%
770 2
 
< 0.1%
769 2
 
< 0.1%
766 61
 
< 0.1%
765 1
 
< 0.1%
764 3
 
< 0.1%
763 1
 
< 0.1%
760 1
 
< 0.1%
756 846
< 0.1%
752 56
 
< 0.1%

i94res
Real number (ℝ)

Distinct229
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean303.28382
Minimum101
Maximum760
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.2 MiB
2022-12-10T21:39:32.770329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile111
Q1131
median213
Q3504
95-th percentile691
Maximum760
Range659
Interquartile range (IQR)373

Descriptive statistics

Standard deviation208.58321
Coefficient of variation (CV)0.68774923
Kurtosis-0.85653379
Mean303.28382
Median Absolute Deviation (MAD)90
Skewness0.84355474
Sum9.3906163 × 108
Variance43506.957
MonotonicityNot monotonic
2022-12-10T21:39:33.168414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
135 368421
 
11.9%
209 249167
 
8.0%
245 185609
 
6.0%
111 185339
 
6.0%
582 179603
 
5.8%
112 156613
 
5.1%
276 136312
 
4.4%
689 134907
 
4.4%
438 112407
 
3.6%
213 107193
 
3.5%
Other values (219) 1280742
41.4%
ValueCountFrequency (%)
101 929
 
< 0.1%
102 117
 
< 0.1%
103 15465
 
0.5%
104 20796
 
0.7%
105 2343
 
0.1%
107 16153
 
0.5%
108 24600
 
0.8%
109 1983
 
0.1%
110 11545
 
0.4%
111 185339
6.0%
ValueCountFrequency (%)
760 2
 
< 0.1%
749 167
 
< 0.1%
748 24
 
< 0.1%
745 2113
0.1%
743 558
 
< 0.1%
736 12
 
< 0.1%
735 423
 
< 0.1%
732 358
 
< 0.1%
723 27
 
< 0.1%
721 551
 
< 0.1%

i94port
Categorical

Distinct299
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 MiB
NYC
485916 
MIA
343941 
LOS
310163 
SFR
 
152586
ORL
 
149195
Other values (294)
1654512 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9288939
Distinct characters31
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)< 0.1%

Sample

1st rowCHI
2nd rowCHI
3rd rowCHI
4th rowCHI
5th rowCHI

Common Values

ValueCountFrequency (%)
NYC 485916
15.7%
MIA 343941
 
11.1%
LOS 310163
 
10.0%
SFR 152586
 
4.9%
ORL 149195
 
4.8%
HHW 142720
 
4.6%
NEW 136122
 
4.4%
CHI 130564
 
4.2%
HOU 101481
 
3.3%
FTL 95977
 
3.1%
Other values (289) 1047648
33.8%

Length

2022-12-10T21:39:33.356656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nyc 485916
15.7%
mia 343941
 
11.1%
los 310163
 
10.0%
sfr 152586
 
4.9%
orl 149195
 
4.8%
hhw 142720
 
4.6%
new 136122
 
4.4%
chi 130564
 
4.2%
hou 101481
 
3.3%
ftl 95977
 
3.1%
Other values (289) 1047648
33.8%

Most occurring characters

ValueCountFrequency (%)
A 962689
 
10.4%
L 864662
 
9.3%
S 787316
 
8.5%
O 728291
 
7.8%
N 695763
 
7.5%
C 673005
 
7.2%
H 604114
 
6.5%
I 538120
 
5.8%
Y 510131
 
5.5%
M 444432
 
4.8%
Other values (21) 2480416
26.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 9284083
99.9%
Decimal Number 4856
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 962689
 
10.4%
L 864662
 
9.3%
S 787316
 
8.5%
O 728291
 
7.8%
N 695763
 
7.5%
C 673005
 
7.2%
H 604114
 
6.5%
I 538120
 
5.8%
Y 510131
 
5.5%
M 444432
 
4.8%
Other values (16) 2475560
26.7%
Decimal Number
ValueCountFrequency (%)
6 2382
49.1%
9 2378
49.0%
5 73
 
1.5%
4 22
 
0.5%
8 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 9284083
99.9%
Common 4856
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 962689
 
10.4%
L 864662
 
9.3%
S 787316
 
8.5%
O 728291
 
7.8%
N 695763
 
7.5%
C 673005
 
7.2%
H 604114
 
6.5%
I 538120
 
5.8%
Y 510131
 
5.5%
M 444432
 
4.8%
Other values (16) 2475560
26.7%
Common
ValueCountFrequency (%)
6 2382
49.1%
9 2378
49.0%
5 73
 
1.5%
4 22
 
0.5%
8 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9288939
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 962689
 
10.4%
L 864662
 
9.3%
S 787316
 
8.5%
O 728291
 
7.8%
N 695763
 
7.5%
C 673005
 
7.2%
H 604114
 
6.5%
I 538120
 
5.8%
Y 510131
 
5.5%
M 444432
 
4.8%
Other values (21) 2480416
26.7%

arrdate
Real number (ℝ)

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20559.849
Minimum20545
Maximum20574
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.2 MiB
2022-12-10T21:39:33.528985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum20545
5-th percentile20546
Q120552
median20560
Q320567
95-th percentile20573
Maximum20574
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7773395
Coefficient of variation (CV)0.00042691654
Kurtosis-1.2040296
Mean20559.849
Median Absolute Deviation (MAD)8
Skewness-0.036326873
Sum6.3659726 × 1010
Variance77.041688
MonotonicityNot monotonic
2022-12-10T21:39:33.710735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20573 128267
 
4.1%
20574 127155
 
4.1%
20572 120971
 
3.9%
20560 114970
 
3.7%
20559 114803
 
3.7%
20567 112883
 
3.6%
20566 110304
 
3.6%
20545 108407
 
3.5%
20558 107557
 
3.5%
20561 106474
 
3.4%
Other values (20) 1944522
62.8%
ValueCountFrequency (%)
20545 108407
3.5%
20546 103196
3.3%
20547 99972
3.2%
20548 97653
3.2%
20549 91514
3.0%
20550 88273
2.9%
20551 99763
3.2%
20552 103660
3.3%
20553 105930
3.4%
20554 104394
3.4%
ValueCountFrequency (%)
20574 127155
4.1%
20573 128267
4.1%
20572 120971
3.9%
20571 99259
3.2%
20570 88100
2.8%
20569 99652
3.2%
20568 100203
3.2%
20567 112883
3.6%
20566 110304
3.6%
20565 105454
3.4%

i94mode
Categorical

Distinct4
Distinct (%)< 0.1%
Missing239
Missing (%)< 0.1%
Memory size47.2 MiB
1.0
2994505 
3.0
 
66660
2.0
 
26349
9.0
 
8560

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9288222
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 2994505
96.7%
3.0 66660
 
2.2%
2.0 26349
 
0.9%
9.0 8560
 
0.3%
(Missing) 239
 
< 0.1%

Length

2022-12-10T21:39:33.921876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-10T21:39:34.134239image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 2994505
96.7%
3.0 66660
 
2.2%
2.0 26349
 
0.9%
9.0 8560
 
0.3%

Most occurring characters

ValueCountFrequency (%)
. 3096074
33.3%
0 3096074
33.3%
1 2994505
32.2%
3 66660
 
0.7%
2 26349
 
0.3%
9 8560
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6192148
66.7%
Other Punctuation 3096074
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3096074
50.0%
1 2994505
48.4%
3 66660
 
1.1%
2 26349
 
0.4%
9 8560
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 3096074
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9288222
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 3096074
33.3%
0 3096074
33.3%
1 2994505
32.2%
3 66660
 
0.7%
2 26349
 
0.3%
9 8560
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9288222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 3096074
33.3%
0 3096074
33.3%
1 2994505
32.2%
3 66660
 
0.7%
2 26349
 
0.3%
9 8560
 
0.1%

i94addr
Categorical

HIGH CARDINALITY
MISSING

Distinct457
Distinct (%)< 0.1%
Missing152592
Missing (%)4.9%
Memory size47.2 MiB
FL
621701 
NY
553677 
CA
470386 
HI
168764 
TX
134321 
Other values (452)
994872 

Length

Max length2
Median length2
Mean length1.9999541
Min length1

Characters and Unicode

Total characters5887307
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique123 ?
Unique (%)< 0.1%

Sample

1st rowVA
2nd rowVA
3rd rowVA
4th rowWA
5th rowDE

Common Values

ValueCountFrequency (%)
FL 621701
20.1%
NY 553677
17.9%
CA 470386
15.2%
HI 168764
 
5.5%
TX 134321
 
4.3%
NV 114609
 
3.7%
GU 94107
 
3.0%
IL 82126
 
2.7%
NJ 76531
 
2.5%
MA 70486
 
2.3%
Other values (447) 557013
18.0%
(Missing) 152592
 
4.9%

Length

2022-12-10T21:39:34.315576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fl 621701
21.1%
ny 553677
18.8%
ca 470386
16.0%
hi 168764
 
5.7%
tx 134321
 
4.6%
nv 114609
 
3.9%
gu 94107
 
3.2%
il 82126
 
2.8%
nj 76531
 
2.6%
ma 70486
 
2.4%
Other values (437) 557013
18.9%

Most occurring characters

ValueCountFrequency (%)
N 837957
14.2%
A 763519
13.0%
L 735142
12.5%
F 622101
10.6%
C 561997
9.5%
Y 560039
9.5%
I 311030
 
5.3%
H 190789
 
3.2%
T 172478
 
2.9%
M 159747
 
2.7%
Other values (27) 972508
16.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5886932
> 99.9%
Decimal Number 268
 
< 0.1%
Other Punctuation 107
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 837957
14.2%
A 763519
13.0%
L 735142
12.5%
F 622101
10.6%
C 561997
9.5%
Y 560039
9.5%
I 311030
 
5.3%
H 190789
 
3.2%
T 172478
 
2.9%
M 159747
 
2.7%
Other values (16) 972133
16.5%
Decimal Number
ValueCountFrequency (%)
9 111
41.4%
0 56
20.9%
1 31
 
11.6%
3 22
 
8.2%
2 18
 
6.7%
7 11
 
4.1%
6 6
 
2.2%
5 5
 
1.9%
4 4
 
1.5%
8 4
 
1.5%
Other Punctuation
ValueCountFrequency (%)
. 107
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5886932
> 99.9%
Common 375
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 837957
14.2%
A 763519
13.0%
L 735142
12.5%
F 622101
10.6%
C 561997
9.5%
Y 560039
9.5%
I 311030
 
5.3%
H 190789
 
3.2%
T 172478
 
2.9%
M 159747
 
2.7%
Other values (16) 972133
16.5%
Common
ValueCountFrequency (%)
9 111
29.6%
. 107
28.5%
0 56
14.9%
1 31
 
8.3%
3 22
 
5.9%
2 18
 
4.8%
7 11
 
2.9%
6 6
 
1.6%
5 5
 
1.3%
4 4
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5887307
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 837957
14.2%
A 763519
13.0%
L 735142
12.5%
F 622101
10.6%
C 561997
9.5%
Y 560039
9.5%
I 311030
 
5.3%
H 190789
 
3.2%
T 172478
 
2.9%
M 159747
 
2.7%
Other values (27) 972508
16.5%

depdate
Real number (ℝ)

HIGH CORRELATION
MISSING
SKEWED

Distinct235
Distinct (%)< 0.1%
Missing142457
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean20573.953
Minimum15176
Maximum45427
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.2 MiB
2022-12-10T21:39:34.536236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum15176
5-th percentile20552
Q120561
median20570
Q320579
95-th percentile20615
Maximum45427
Range30251
Interquartile range (IQR)18

Descriptive statistics

Standard deviation29.356968
Coefficient of variation (CV)0.0014268998
Kurtosis238342.98
Mean20573.953
Median Absolute Deviation (MAD)9
Skewness301.49121
Sum6.0772494 × 1010
Variance861.8316
MonotonicityNot monotonic
2022-12-10T21:39:34.783030image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20574 102689
 
3.3%
20575 102510
 
3.3%
20573 98048
 
3.2%
20567 95936
 
3.1%
20568 94075
 
3.0%
20566 92317
 
3.0%
20561 88298
 
2.9%
20560 87819
 
2.8%
20576 87417
 
2.8%
20559 85894
 
2.8%
Other values (225) 2018853
65.2%
(Missing) 142457
 
4.6%
ValueCountFrequency (%)
15176 1
 
< 0.1%
19095 5
< 0.1%
19097 1
 
< 0.1%
19835 1
 
< 0.1%
19837 1
 
< 0.1%
19860 1
 
< 0.1%
20181 1
 
< 0.1%
20186 1
 
< 0.1%
20194 1
 
< 0.1%
20196 1
 
< 0.1%
ValueCountFrequency (%)
45427 1
 
< 0.1%
39935 1
 
< 0.1%
21919 1
 
< 0.1%
20716 156
 
< 0.1%
20715 997
< 0.1%
20714 1137
< 0.1%
20713 794
< 0.1%
20712 1109
< 0.1%
20711 870
< 0.1%
20710 668
< 0.1%

i94bir
Real number (ℝ)

Distinct112
Distinct (%)< 0.1%
Missing802
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean41.767614
Minimum-3
Maximum114
Zeros765
Zeros (%)< 0.1%
Negative1
Negative (%)< 0.1%
Memory size47.2 MiB
2022-12-10T21:39:35.010733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-3
5-th percentile11
Q130
median41
Q354
95-th percentile70
Maximum114
Range117
Interquartile range (IQR)24

Descriptive statistics

Standard deviation17.420261
Coefficient of variation (CV)0.41707578
Kurtosis-0.42093114
Mean41.767614
Median Absolute Deviation (MAD)12
Skewness-0.036911348
Sum1.2929211 × 108
Variance303.46548
MonotonicityNot monotonic
2022-12-10T21:39:35.245930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 71958
 
2.3%
33 70415
 
2.3%
31 70409
 
2.3%
34 70251
 
2.3%
32 69809
 
2.3%
35 69626
 
2.2%
36 67960
 
2.2%
29 67762
 
2.2%
40 66568
 
2.1%
37 66494
 
2.1%
Other values (102) 2404259
77.6%
ValueCountFrequency (%)
-3 1
 
< 0.1%
0 765
 
< 0.1%
1 12747
0.4%
2 14756
0.5%
3 12704
0.4%
4 14411
0.5%
5 15129
0.5%
6 15773
0.5%
7 14233
0.5%
8 14607
0.5%
ValueCountFrequency (%)
114 1
 
< 0.1%
111 1
 
< 0.1%
110 1
 
< 0.1%
109 2
< 0.1%
108 2
< 0.1%
107 1
 
< 0.1%
105 2
< 0.1%
103 1
 
< 0.1%
102 4
< 0.1%
101 2
< 0.1%

i94visa
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 MiB
2.0
2530868 
1.0
522079 
3.0
 
43366

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9288939
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row1.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 2530868
81.7%
1.0 522079
 
16.9%
3.0 43366
 
1.4%

Length

2022-12-10T21:39:35.444874image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-10T21:39:35.634965image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 2530868
81.7%
1.0 522079
 
16.9%
3.0 43366
 
1.4%

Most occurring characters

ValueCountFrequency (%)
. 3096313
33.3%
0 3096313
33.3%
2 2530868
27.2%
1 522079
 
5.6%
3 43366
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6192626
66.7%
Other Punctuation 3096313
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3096313
50.0%
2 2530868
40.9%
1 522079
 
8.4%
3 43366
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 3096313
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9288939
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 3096313
33.3%
0 3096313
33.3%
2 2530868
27.2%
1 522079
 
5.6%
3 43366
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9288939
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 3096313
33.3%
0 3096313
33.3%
2 2530868
27.2%
1 522079
 
5.6%
3 43366
 
0.5%

count
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 MiB
1.0
3096313 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters9288939
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 3096313
100.0%

Length

2022-12-10T21:39:35.801980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-10T21:39:35.984212image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 3096313
100.0%

Most occurring characters

ValueCountFrequency (%)
1 3096313
33.3%
. 3096313
33.3%
0 3096313
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6192626
66.7%
Other Punctuation 3096313
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3096313
50.0%
0 3096313
50.0%
Other Punctuation
ValueCountFrequency (%)
. 3096313
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9288939
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3096313
33.3%
. 3096313
33.3%
0 3096313
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9288939
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3096313
33.3%
. 3096313
33.3%
0 3096313
33.3%

dtadfile
Real number (ℝ)

Distinct117
Distinct (%)< 0.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean20160425
Minimum20130811
Maximum20160919
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.2 MiB
2022-12-10T21:39:36.189590image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum20130811
5-th percentile20160402
Q120160409
median20160417
Q320160424
95-th percentile20160430
Maximum20160919
Range30108
Interquartile range (IQR)15

Descriptive statistics

Standard deviation50.015134
Coefficient of variation (CV)2.4808572 × 10-6
Kurtosis39717.382
Mean20160425
Median Absolute Deviation (MAD)8
Skewness-62.603381
Sum6.2422965 × 1013
Variance2501.5137
MonotonicityNot monotonic
2022-12-10T21:39:36.432226image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20160430 125570
 
4.1%
20160429 120497
 
3.9%
20160417 119296
 
3.9%
20160428 116601
 
3.8%
20160415 109746
 
3.5%
20160423 107079
 
3.5%
20160414 106306
 
3.4%
20160422 105575
 
3.4%
20160401 103231
 
3.3%
20160409 102239
 
3.3%
Other values (107) 1980172
64.0%
ValueCountFrequency (%)
20130811 1
 
< 0.1%
20160401 103231
3.3%
20160402 98915
3.2%
20160403 94852
3.1%
20160404 94511
3.1%
20160405 86195
2.8%
20160406 85177
2.8%
20160407 95901
3.1%
20160408 99181
3.2%
20160409 102239
3.3%
ValueCountFrequency (%)
20160919 1
 
< 0.1%
20160918 14
 
< 0.1%
20160917 22
 
< 0.1%
20160916 18
 
< 0.1%
20160915 3
 
< 0.1%
20160914 8
 
< 0.1%
20160913 5
 
< 0.1%
20160909 125
< 0.1%
20160906 7
 
< 0.1%
20160902 9
 
< 0.1%

visapost
Categorical

HIGH CARDINALITY
MISSING

Distinct530
Distinct (%)< 0.1%
Missing1881250
Missing (%)60.8%
Memory size47.2 MiB
MEX
 
84720
SPL
 
65678
BNS
 
62032
GUZ
 
48298
BGT
 
46074
Other values (525)
908261 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3645189
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)< 0.1%

Sample

1st rowBEJ
2nd rowBEJ
3rd rowBEJ
4th rowBEJ
5th rowBEJ

Common Values

ValueCountFrequency (%)
MEX 84720
 
2.7%
SPL 65678
 
2.1%
BNS 62032
 
2.0%
GUZ 48298
 
1.6%
BGT 46074
 
1.5%
CRS 37137
 
1.2%
BEJ 36703
 
1.2%
SHG 35507
 
1.1%
GDL 30970
 
1.0%
RDJ 29943
 
1.0%
Other values (520) 738001
 
23.8%
(Missing) 1881250
60.8%

Length

2022-12-10T21:39:36.667865image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mex 84720
 
7.0%
spl 65678
 
5.4%
bns 62032
 
5.1%
guz 48298
 
4.0%
bgt 46074
 
3.8%
crs 37137
 
3.1%
bej 36703
 
3.0%
shg 35507
 
2.9%
gdl 30970
 
2.5%
rdj 29943
 
2.5%
Other values (520) 738001
60.7%

Most occurring characters

ValueCountFrequency (%)
S 390557
 
10.7%
M 282799
 
7.8%
G 279336
 
7.7%
B 276638
 
7.6%
N 259522
 
7.1%
T 237480
 
6.5%
L 227298
 
6.2%
R 204924
 
5.6%
D 196076
 
5.4%
E 166542
 
4.6%
Other values (17) 1124017
30.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3643377
> 99.9%
Decimal Number 1812
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 390557
 
10.7%
M 282799
 
7.8%
G 279336
 
7.7%
B 276638
 
7.6%
N 259522
 
7.1%
T 237480
 
6.5%
L 227298
 
6.2%
R 204924
 
5.6%
D 196076
 
5.4%
E 166542
 
4.6%
Other values (16) 1122205
30.8%
Decimal Number
ValueCountFrequency (%)
9 1812
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3643377
> 99.9%
Common 1812
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 390557
 
10.7%
M 282799
 
7.8%
G 279336
 
7.7%
B 276638
 
7.6%
N 259522
 
7.1%
T 237480
 
6.5%
L 227298
 
6.2%
R 204924
 
5.6%
D 196076
 
5.4%
E 166542
 
4.6%
Other values (16) 1122205
30.8%
Common
ValueCountFrequency (%)
9 1812
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3645189
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 390557
 
10.7%
M 282799
 
7.8%
G 279336
 
7.7%
B 276638
 
7.6%
N 259522
 
7.1%
T 237480
 
6.5%
L 227298
 
6.2%
R 204924
 
5.6%
D 196076
 
5.4%
E 166542
 
4.6%
Other values (17) 1124017
30.8%

occup
Categorical

HIGH CARDINALITY
MISSING

Distinct111
Distinct (%)1.4%
Missing3088187
Missing (%)99.7%
Memory size47.2 MiB
STU
4719 
OTH
661 
NRR
 
345
MKT
 
280
EXA
 
196
Other values (106)
1925 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters24378
Distinct characters32
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)0.2%

Sample

1st rowSTU
2nd rowSTU
3rd rowSTU
4th rowNRR
5th rowSTU

Common Values

ValueCountFrequency (%)
STU 4719
 
0.2%
OTH 661
 
< 0.1%
NRR 345
 
< 0.1%
MKT 280
 
< 0.1%
EXA 196
 
< 0.1%
GLS 189
 
< 0.1%
ULS 175
 
< 0.1%
ADM 125
 
< 0.1%
TIE 124
 
< 0.1%
MVC 110
 
< 0.1%
Other values (101) 1202
 
< 0.1%
(Missing) 3088187
99.7%

Length

2022-12-10T21:39:36.857541image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
stu 4719
58.1%
oth 661
 
8.1%
nrr 345
 
4.2%
mkt 280
 
3.4%
exa 196
 
2.4%
gls 189
 
2.3%
uls 175
 
2.2%
adm 125
 
1.5%
tie 124
 
1.5%
mvc 110
 
1.4%
Other values (101) 1202
 
14.8%

Most occurring characters

ValueCountFrequency (%)
T 6150
25.2%
S 5241
21.5%
U 4944
20.3%
H 893
 
3.7%
R 873
 
3.6%
O 803
 
3.3%
E 727
 
3.0%
M 691
 
2.8%
L 546
 
2.2%
N 542
 
2.2%
Other values (22) 2968
12.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 24227
99.4%
Decimal Number 151
 
0.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 6150
25.4%
S 5241
21.6%
U 4944
20.4%
H 893
 
3.7%
R 873
 
3.6%
O 803
 
3.3%
E 727
 
3.0%
M 691
 
2.9%
L 546
 
2.3%
N 542
 
2.2%
Other values (14) 2817
11.6%
Decimal Number
ValueCountFrequency (%)
9 85
56.3%
1 33
 
21.9%
5 14
 
9.3%
8 7
 
4.6%
0 5
 
3.3%
2 4
 
2.6%
4 2
 
1.3%
3 1
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 24227
99.4%
Common 151
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 6150
25.4%
S 5241
21.6%
U 4944
20.4%
H 893
 
3.7%
R 873
 
3.6%
O 803
 
3.3%
E 727
 
3.0%
M 691
 
2.9%
L 546
 
2.3%
N 542
 
2.2%
Other values (14) 2817
11.6%
Common
ValueCountFrequency (%)
9 85
56.3%
1 33
 
21.9%
5 14
 
9.3%
8 7
 
4.6%
0 5
 
3.3%
2 4
 
2.6%
4 2
 
1.3%
3 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24378
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 6150
25.2%
S 5241
21.5%
U 4944
20.3%
H 893
 
3.7%
R 873
 
3.6%
O 803
 
3.3%
E 727
 
3.0%
M 691
 
2.8%
L 546
 
2.2%
N 542
 
2.2%
Other values (22) 2968
12.2%

entdepa
Categorical

Distinct13
Distinct (%)< 0.1%
Missing238
Missing (%)< 0.1%
Memory size47.2 MiB
G
2399582 
O
413057 
A
 
108560
Z
 
64864
T
 
61144
Other values (8)
 
48868

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters3096075
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowG
2nd rowG
3rd rowG
4th rowG
5th rowG

Common Values

ValueCountFrequency (%)
G 2399582
77.5%
O 413057
 
13.3%
A 108560
 
3.5%
Z 64864
 
2.1%
T 61144
 
2.0%
K 17076
 
0.6%
P 14397
 
0.5%
H 14341
 
0.5%
U 2371
 
0.1%
B 401
 
< 0.1%
Other values (3) 282
 
< 0.1%

Length

2022-12-10T21:39:37.031362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
g 2399582
77.5%
o 413057
 
13.3%
a 108560
 
3.5%
z 64864
 
2.1%
t 61144
 
2.0%
k 17076
 
0.6%
p 14397
 
0.5%
h 14341
 
0.5%
u 2371
 
0.1%
b 401
 
< 0.1%
Other values (3) 282
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
G 2399582
77.5%
O 413057
 
13.3%
A 108560
 
3.5%
Z 64864
 
2.1%
T 61144
 
2.0%
K 17076
 
0.6%
P 14397
 
0.5%
H 14341
 
0.5%
U 2371
 
0.1%
B 401
 
< 0.1%
Other values (3) 282
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3096075
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 2399582
77.5%
O 413057
 
13.3%
A 108560
 
3.5%
Z 64864
 
2.1%
T 61144
 
2.0%
K 17076
 
0.6%
P 14397
 
0.5%
H 14341
 
0.5%
U 2371
 
0.1%
B 401
 
< 0.1%
Other values (3) 282
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 3096075
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 2399582
77.5%
O 413057
 
13.3%
A 108560
 
3.5%
Z 64864
 
2.1%
T 61144
 
2.0%
K 17076
 
0.6%
P 14397
 
0.5%
H 14341
 
0.5%
U 2371
 
0.1%
B 401
 
< 0.1%
Other values (3) 282
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3096075
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 2399582
77.5%
O 413057
 
13.3%
A 108560
 
3.5%
Z 64864
 
2.1%
T 61144
 
2.0%
K 17076
 
0.6%
P 14397
 
0.5%
H 14341
 
0.5%
U 2371
 
0.1%
B 401
 
< 0.1%
Other values (3) 282
 
< 0.1%

entdepd
Categorical

HIGH CORRELATION
MISSING

Distinct12
Distinct (%)< 0.1%
Missing138429
Missing (%)4.5%
Memory size47.2 MiB
O
2513632 
I
 
99846
D
 
96518
N
 
76192
K
 
70624
Other values (7)
 
101072

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2957884
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowO
2nd rowO
3rd rowO
4th rowO
5th rowO

Common Values

ValueCountFrequency (%)
O 2513632
81.2%
I 99846
 
3.2%
D 96518
 
3.1%
N 76192
 
2.5%
K 70624
 
2.3%
Q 52729
 
1.7%
R 41879
 
1.4%
W 3887
 
0.1%
J 1758
 
0.1%
V 762
 
< 0.1%
Other values (2) 57
 
< 0.1%
(Missing) 138429
 
4.5%

Length

2022-12-10T21:39:37.208707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
o 2513632
85.0%
i 99846
 
3.4%
d 96518
 
3.3%
n 76192
 
2.6%
k 70624
 
2.4%
q 52729
 
1.8%
r 41879
 
1.4%
w 3887
 
0.1%
j 1758
 
0.1%
v 762
 
< 0.1%
Other values (2) 57
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
O 2513632
85.0%
I 99846
 
3.4%
D 96518
 
3.3%
N 76192
 
2.6%
K 70624
 
2.4%
Q 52729
 
1.8%
R 41879
 
1.4%
W 3887
 
0.1%
J 1758
 
0.1%
V 762
 
< 0.1%
Other values (2) 57
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2957884
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 2513632
85.0%
I 99846
 
3.4%
D 96518
 
3.3%
N 76192
 
2.6%
K 70624
 
2.4%
Q 52729
 
1.8%
R 41879
 
1.4%
W 3887
 
0.1%
J 1758
 
0.1%
V 762
 
< 0.1%
Other values (2) 57
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 2957884
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 2513632
85.0%
I 99846
 
3.4%
D 96518
 
3.3%
N 76192
 
2.6%
K 70624
 
2.4%
Q 52729
 
1.8%
R 41879
 
1.4%
W 3887
 
0.1%
J 1758
 
0.1%
V 762
 
< 0.1%
Other values (2) 57
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2957884
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 2513632
85.0%
I 99846
 
3.4%
D 96518
 
3.3%
N 76192
 
2.6%
K 70624
 
2.4%
Q 52729
 
1.8%
R 41879
 
1.4%
W 3887
 
0.1%
J 1758
 
0.1%
V 762
 
< 0.1%
Other values (2) 57
 
< 0.1%

entdepu
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.5%
Missing3095921
Missing (%)> 99.9%
Memory size47.2 MiB
U
391 
Y
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters392
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st rowU
2nd rowU
3rd rowU
4th rowU
5th rowU

Common Values

ValueCountFrequency (%)
U 391
 
< 0.1%
Y 1
 
< 0.1%
(Missing) 3095921
> 99.9%

Length

2022-12-10T21:39:37.371732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-10T21:39:37.547227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
u 391
99.7%
y 1
 
0.3%

Most occurring characters

ValueCountFrequency (%)
U 391
99.7%
Y 1
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 392
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 391
99.7%
Y 1
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 392
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 391
99.7%
Y 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 391
99.7%
Y 1
 
0.3%

matflag
Categorical

CONSTANT
MISSING

Distinct1
Distinct (%)< 0.1%
Missing138429
Missing (%)4.5%
Memory size47.2 MiB
M
2957884 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2957884
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
M 2957884
95.5%
(Missing) 138429
 
4.5%

Length

2022-12-10T21:39:37.691524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-10T21:39:37.859577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
m 2957884
100.0%

Most occurring characters

ValueCountFrequency (%)
M 2957884
100.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2957884
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 2957884
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2957884
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 2957884
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2957884
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 2957884
100.0%

biryear
Real number (ℝ)

Distinct112
Distinct (%)< 0.1%
Missing802
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1974.2324
Minimum1902
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.2 MiB
2022-12-10T21:39:38.037494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1902
5-th percentile1946
Q11962
median1975
Q31986
95-th percentile2005
Maximum2019
Range117
Interquartile range (IQR)24

Descriptive statistics

Standard deviation17.420261
Coefficient of variation (CV)0.0088238146
Kurtosis-0.42093114
Mean1974.2324
Median Absolute Deviation (MAD)12
Skewness0.036911348
Sum6.1112581 × 109
Variance303.46548
MonotonicityNot monotonic
2022-12-10T21:39:38.308786image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1986 71958
 
2.3%
1983 70415
 
2.3%
1985 70409
 
2.3%
1982 70251
 
2.3%
1984 69809
 
2.3%
1981 69626
 
2.2%
1980 67960
 
2.2%
1987 67762
 
2.2%
1976 66568
 
2.1%
1979 66494
 
2.1%
Other values (102) 2404259
77.6%
ValueCountFrequency (%)
1902 1
 
< 0.1%
1905 1
 
< 0.1%
1906 1
 
< 0.1%
1907 2
< 0.1%
1908 2
< 0.1%
1909 1
 
< 0.1%
1911 2
< 0.1%
1913 1
 
< 0.1%
1914 4
< 0.1%
1915 2
< 0.1%
ValueCountFrequency (%)
2019 1
 
< 0.1%
2016 765
 
< 0.1%
2015 12747
0.4%
2014 14756
0.5%
2013 12704
0.4%
2012 14411
0.5%
2011 15129
0.5%
2010 15773
0.5%
2009 14233
0.5%
2008 14607
0.5%

dtaddto
Categorical

Distinct777
Distinct (%)< 0.1%
Missing477
Missing (%)< 0.1%
Memory size47.2 MiB
07282016
 
67889
07272016
 
64789
07152016
 
63438
07262016
 
60234
07212016
 
58358
Other values (772)
2781128 

Length

Max length8
Median length8
Mean length7.9267661
Min length3

Characters and Unicode

Total characters24539968
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)< 0.1%

Sample

1st row10252016
2nd row10252016
3rd row10252016
4th row10252016
5th row10252016

Common Values

ValueCountFrequency (%)
07282016 67889
 
2.2%
07272016 64789
 
2.1%
07152016 63438
 
2.0%
07262016 60234
 
1.9%
07212016 58358
 
1.9%
07072016 57234
 
1.8%
07132016 56363
 
1.8%
06292016 56349
 
1.8%
06302016 56134
 
1.8%
07082016 56108
 
1.8%
Other values (767) 2498940
80.7%

Length

2022-12-10T21:39:38.531039image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
07282016 67889
 
2.2%
07272016 64789
 
2.1%
07152016 63438
 
2.0%
07262016 60234
 
1.9%
07212016 58358
 
1.9%
07072016 57234
 
1.8%
07132016 56363
 
1.8%
06292016 56349
 
1.8%
06302016 56134
 
1.8%
07082016 56108
 
1.8%
Other values (770) 2498943
80.7%

Most occurring characters

ValueCountFrequency (%)
0 7262282
29.6%
1 5595137
22.8%
2 4437861
18.1%
6 3498236
14.3%
7 1798715
 
7.3%
3 404160
 
1.6%
5 379578
 
1.5%
9 365010
 
1.5%
8 349001
 
1.4%
4 313952
 
1.3%
Other values (4) 136036
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24403932
99.4%
Uppercase Letter 90687
 
0.4%
Other Punctuation 45344
 
0.2%
Space Separator 5
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 7262282
29.8%
1 5595137
22.9%
2 4437861
18.2%
6 3498236
14.3%
7 1798715
 
7.4%
3 404160
 
1.7%
5 379578
 
1.6%
9 365010
 
1.5%
8 349001
 
1.4%
4 313952
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
D 45344
50.0%
S 45343
50.0%
Other Punctuation
ValueCountFrequency (%)
/ 45344
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24449281
99.6%
Latin 90687
 
0.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 7262282
29.7%
1 5595137
22.9%
2 4437861
18.2%
6 3498236
14.3%
7 1798715
 
7.4%
3 404160
 
1.7%
5 379578
 
1.6%
9 365010
 
1.5%
8 349001
 
1.4%
4 313952
 
1.3%
Other values (2) 45349
 
0.2%
Latin
ValueCountFrequency (%)
D 45344
50.0%
S 45343
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24539968
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7262282
29.6%
1 5595137
22.8%
2 4437861
18.1%
6 3498236
14.3%
7 1798715
 
7.3%
3 404160
 
1.6%
5 379578
 
1.5%
9 365010
 
1.5%
8 349001
 
1.4%
4 313952
 
1.3%
Other values (4) 136036
 
0.6%

gender
Categorical

Distinct4
Distinct (%)< 0.1%
Missing414269
Missing (%)13.4%
Memory size47.2 MiB
M
1377224 
F
1302743 
X
 
1610
U
 
467

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2682044
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowM
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
M 1377224
44.5%
F 1302743
42.1%
X 1610
 
0.1%
U 467
 
< 0.1%
(Missing) 414269
 
13.4%

Length

2022-12-10T21:39:38.695153image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-10T21:39:38.878707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
m 1377224
51.3%
f 1302743
48.6%
x 1610
 
0.1%
u 467
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
M 1377224
51.3%
F 1302743
48.6%
X 1610
 
0.1%
U 467
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2682044
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 1377224
51.3%
F 1302743
48.6%
X 1610
 
0.1%
U 467
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 2682044
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 1377224
51.3%
F 1302743
48.6%
X 1610
 
0.1%
U 467
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2682044
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 1377224
51.3%
F 1302743
48.6%
X 1610
 
0.1%
U 467
 
< 0.1%

insnum
Categorical

HIGH CARDINALITY
MISSING

Distinct1913
Distinct (%)1.7%
Missing2982605
Missing (%)96.3%
Memory size47.2 MiB
3692
 
2155
3697
 
2033
3703
 
1986
3893
 
1866
3661
 
1820
Other values (1908)
103848 

Length

Max length6
Median length4
Mean length3.9993932
Min length1

Characters and Unicode

Total characters454763
Distinct characters34
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique744 ?
Unique (%)0.7%

Sample

1st row7805
2nd row7805
3rd row5113
4th row5113
5th row5057

Common Values

ValueCountFrequency (%)
3692 2155
 
0.1%
3697 2033
 
0.1%
3703 1986
 
0.1%
3893 1866
 
0.1%
3661 1820
 
0.1%
3693 1690
 
0.1%
3939 1680
 
0.1%
3672 1678
 
0.1%
3882 1673
 
0.1%
3943 1662
 
0.1%
Other values (1903) 95465
 
3.1%
(Missing) 2982605
96.3%

Length

2022-12-10T21:39:39.061725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
3692 2155
 
1.9%
3697 2033
 
1.8%
3703 1986
 
1.7%
3893 1866
 
1.6%
3661 1820
 
1.6%
3693 1690
 
1.5%
3939 1680
 
1.5%
3672 1678
 
1.5%
3882 1673
 
1.5%
3943 1662
 
1.5%
Other values (1903) 95465
84.0%

Most occurring characters

ValueCountFrequency (%)
3 134774
29.6%
9 67871
14.9%
6 55410
12.2%
8 45323
 
10.0%
7 34317
 
7.5%
4 30600
 
6.7%
5 28628
 
6.3%
0 22052
 
4.8%
2 19847
 
4.4%
1 14773
 
3.2%
Other values (24) 1168
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 453595
99.7%
Uppercase Letter 1168
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 254
21.7%
K 254
21.7%
U 248
21.2%
T 45
 
3.9%
A 35
 
3.0%
M 32
 
2.7%
G 32
 
2.7%
L 27
 
2.3%
D 25
 
2.1%
J 24
 
2.1%
Other values (14) 192
16.4%
Decimal Number
ValueCountFrequency (%)
3 134774
29.7%
9 67871
15.0%
6 55410
12.2%
8 45323
 
10.0%
7 34317
 
7.6%
4 30600
 
6.7%
5 28628
 
6.3%
0 22052
 
4.9%
2 19847
 
4.4%
1 14773
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 453595
99.7%
Latin 1168
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 254
21.7%
K 254
21.7%
U 248
21.2%
T 45
 
3.9%
A 35
 
3.0%
M 32
 
2.7%
G 32
 
2.7%
L 27
 
2.3%
D 25
 
2.1%
J 24
 
2.1%
Other values (14) 192
16.4%
Common
ValueCountFrequency (%)
3 134774
29.7%
9 67871
15.0%
6 55410
12.2%
8 45323
 
10.0%
7 34317
 
7.6%
4 30600
 
6.7%
5 28628
 
6.3%
0 22052
 
4.9%
2 19847
 
4.4%
1 14773
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 454763
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 134774
29.6%
9 67871
14.9%
6 55410
12.2%
8 45323
 
10.0%
7 34317
 
7.5%
4 30600
 
6.7%
5 28628
 
6.3%
0 22052
 
4.8%
2 19847
 
4.4%
1 14773
 
3.2%
Other values (24) 1168
 
0.3%

airline
Categorical

HIGH CARDINALITY
MISSING

Distinct534
Distinct (%)< 0.1%
Missing83627
Missing (%)2.7%
Memory size47.2 MiB
AA
310091 
UA
264271 
DL
252526 
BA
190997 
LH
 
120556
Other values (529)
1874245 

Length

Max length3
Median length2
Mean length2.0144366
Min length1

Characters and Unicode

Total characters6068865
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique136 ?
Unique (%)< 0.1%

Sample

1st rowAA
2nd rowAA
3rd rowAA
4th rowAA
5th rowDL

Common Values

ValueCountFrequency (%)
AA 310091
 
10.0%
UA 264271
 
8.5%
DL 252526
 
8.2%
BA 190997
 
6.2%
LH 120556
 
3.9%
VS 113384
 
3.7%
AF 81113
 
2.6%
KE 71047
 
2.3%
JL 69075
 
2.2%
AM 60307
 
1.9%
Other values (524) 1479319
47.8%
(Missing) 83627
 
2.7%

Length

2022-12-10T21:39:39.233794image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aa 310091
 
10.3%
ua 264271
 
8.8%
dl 252526
 
8.4%
ba 190997
 
6.3%
lh 120556
 
4.0%
vs 113384
 
3.8%
af 81113
 
2.7%
ke 71047
 
2.4%
jl 69075
 
2.3%
am 60307
 
2.0%
Other values (522) 1479319
49.1%

Most occurring characters

ValueCountFrequency (%)
A 1495745
24.6%
L 591473
 
9.7%
U 350380
 
5.8%
B 326914
 
5.4%
D 311780
 
5.1%
K 270109
 
4.5%
S 265466
 
4.4%
V 224290
 
3.7%
E 210965
 
3.5%
H 205568
 
3.4%
Other values (27) 1816175
29.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5885564
97.0%
Decimal Number 175945
 
2.9%
Other Punctuation 7356
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1495745
25.4%
L 591473
 
10.0%
U 350380
 
6.0%
B 326914
 
5.6%
D 311780
 
5.3%
K 270109
 
4.6%
S 265466
 
4.5%
V 224290
 
3.8%
E 210965
 
3.6%
H 205568
 
3.5%
Other values (16) 1632874
27.7%
Decimal Number
ValueCountFrequency (%)
4 63988
36.4%
6 49589
28.2%
7 23902
 
13.6%
3 22990
 
13.1%
9 7070
 
4.0%
2 5038
 
2.9%
5 1928
 
1.1%
8 864
 
0.5%
0 500
 
0.3%
1 76
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
* 7356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5885564
97.0%
Common 183301
 
3.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1495745
25.4%
L 591473
 
10.0%
U 350380
 
6.0%
B 326914
 
5.6%
D 311780
 
5.3%
K 270109
 
4.6%
S 265466
 
4.5%
V 224290
 
3.8%
E 210965
 
3.6%
H 205568
 
3.5%
Other values (16) 1632874
27.7%
Common
ValueCountFrequency (%)
4 63988
34.9%
6 49589
27.1%
7 23902
 
13.0%
3 22990
 
12.5%
* 7356
 
4.0%
9 7070
 
3.9%
2 5038
 
2.7%
5 1928
 
1.1%
8 864
 
0.5%
0 500
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6068865
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1495745
24.6%
L 591473
 
9.7%
U 350380
 
5.8%
B 326914
 
5.4%
D 311780
 
5.1%
K 270109
 
4.5%
S 265466
 
4.4%
V 224290
 
3.7%
E 210965
 
3.5%
H 205568
 
3.4%
Other values (27) 1816175
29.9%

admnum
Real number (ℝ)

Distinct3075579
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.082885 × 1010
Minimum0
Maximum9.9915566 × 1010
Zeros68
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size47.2 MiB
2022-12-10T21:39:39.463856image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.6608464 × 1010
Q15.6035228 × 1010
median5.9360939 × 1010
Q39.350987 × 1010
95-th percentile9.4729123 × 1010
Maximum9.9915566 × 1010
Range9.9915566 × 1010
Interquartile range (IQR)3.7474641 × 1010

Descriptive statistics

Standard deviation2.2154416 × 1010
Coefficient of variation (CV)0.31278802
Kurtosis0.53739406
Mean7.082885 × 1010
Median Absolute Deviation (MAD)3.9545292 × 109
Skewness-0.63161887
Sum2.1930829 × 1017
Variance4.9081815 × 1020
MonotonicityNot monotonic
2022-12-10T21:39:39.706754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 68
 
< 0.1%
7.812054623 × 101011
 
< 0.1%
4.652077483 × 10109
 
< 0.1%
8.924999063 × 10109
 
< 0.1%
8.904084763 × 10108
 
< 0.1%
4.701040863 × 10108
 
< 0.1%
5.603642833 × 10107
 
< 0.1%
8.581902733 × 10107
 
< 0.1%
3.697806763 × 10107
 
< 0.1%
9.322572153 × 10107
 
< 0.1%
Other values (3075569) 3096172
> 99.9%
ValueCountFrequency (%)
0 68
< 0.1%
27 1
 
< 0.1%
1218224 1
 
< 0.1%
1219024 1
 
< 0.1%
1219124 1
 
< 0.1%
1219224 1
 
< 0.1%
1219324 1
 
< 0.1%
1219424 1
 
< 0.1%
1222424 1
 
< 0.1%
1226124 1
 
< 0.1%
ValueCountFrequency (%)
9.991556593 × 10101
< 0.1%
9.888880022 × 10101
< 0.1%
9.888879932 × 10101
< 0.1%
9.888879842 × 10101
< 0.1%
9.888879752 × 10101
< 0.1%
9.888879662 × 10101
< 0.1%
9.888879572 × 10101
< 0.1%
9.888878742 × 10101
< 0.1%
9.888878652 × 10101
< 0.1%
9.888878562 × 10101
< 0.1%

fltno
Categorical

Distinct7152
Distinct (%)0.2%
Missing19549
Missing (%)0.6%
Memory size47.2 MiB
LAND
 
44297
00006
 
30942
00001
 
29487
00007
 
23999
00008
 
22783
Other values (7147)
2925256 

Length

Max length5
Median length5
Mean length4.9371242
Min length1

Characters and Unicode

Total characters15190366
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1310 ?
Unique (%)< 0.1%

Sample

1st row00262
2nd row00262
3rd row00262
4th row00262
5th row00188

Common Values

ValueCountFrequency (%)
LAND 44297
 
1.4%
00006 30942
 
1.0%
00001 29487
 
1.0%
00007 23999
 
0.8%
00008 22783
 
0.7%
00003 21458
 
0.7%
00011 20238
 
0.7%
00005 20106
 
0.6%
00012 18992
 
0.6%
00015 18000
 
0.6%
Other values (7142) 2826462
91.3%
(Missing) 19549
 
0.6%

Length

2022-12-10T21:39:39.965499image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
land 44297
 
1.4%
00006 30942
 
1.0%
00001 29487
 
1.0%
00007 23999
 
0.8%
00008 22783
 
0.7%
00003 21458
 
0.7%
00011 20238
 
0.7%
00005 20106
 
0.7%
00012 18992
 
0.6%
00015 18000
 
0.6%
Other values (7142) 2826463
91.9%

Most occurring characters

ValueCountFrequency (%)
0 7229357
47.6%
1 1321534
 
8.7%
2 1111407
 
7.3%
4 851247
 
5.6%
8 822253
 
5.4%
7 777395
 
5.1%
9 730173
 
4.8%
6 726262
 
4.8%
5 707330
 
4.7%
3 674832
 
4.4%
Other values (28) 238576
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14951790
98.4%
Uppercase Letter 238571
 
1.6%
Dash Punctuation 4
 
< 0.1%
Space Separator 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 58858
24.7%
N 53628
22.5%
D 50442
21.1%
L 44963
18.8%
M 7471
 
3.1%
C 5530
 
2.3%
X 2236
 
0.9%
B 1718
 
0.7%
R 1551
 
0.7%
S 1538
 
0.6%
Other values (16) 10636
 
4.5%
Decimal Number
ValueCountFrequency (%)
0 7229357
48.4%
1 1321534
 
8.8%
2 1111407
 
7.4%
4 851247
 
5.7%
8 822253
 
5.5%
7 777395
 
5.2%
9 730173
 
4.9%
6 726262
 
4.9%
5 707330
 
4.7%
3 674832
 
4.5%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14951795
98.4%
Latin 238571
 
1.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 58858
24.7%
N 53628
22.5%
D 50442
21.1%
L 44963
18.8%
M 7471
 
3.1%
C 5530
 
2.3%
X 2236
 
0.9%
B 1718
 
0.7%
R 1551
 
0.7%
S 1538
 
0.6%
Other values (16) 10636
 
4.5%
Common
ValueCountFrequency (%)
0 7229357
48.4%
1 1321534
 
8.8%
2 1111407
 
7.4%
4 851247
 
5.7%
8 822253
 
5.5%
7 777395
 
5.2%
9 730173
 
4.9%
6 726262
 
4.9%
5 707330
 
4.7%
3 674832
 
4.5%
Other values (2) 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15190366
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 7229357
47.6%
1 1321534
 
8.7%
2 1111407
 
7.3%
4 851247
 
5.6%
8 822253
 
5.4%
7 777395
 
5.1%
9 730173
 
4.8%
6 726262
 
4.8%
5 707330
 
4.7%
3 674832
 
4.4%
Other values (28) 238576
 
1.6%

visatype
Categorical

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 MiB
WT
1309059 
B2
1117897 
WB
282983 
B1
212410 
GMT
 
89133
Other values (12)
 
84831

Length

Max length3
Median length2
Mean length2.0278163
Min length1

Characters and Unicode

Total characters6278754
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB2
2nd rowB2
3rd rowB2
4th rowB1
5th rowB2

Common Values

ValueCountFrequency (%)
WT 1309059
42.3%
B2 1117897
36.1%
WB 282983
 
9.1%
B1 212410
 
6.9%
GMT 89133
 
2.9%
F1 39016
 
1.3%
E2 19383
 
0.6%
CP 14758
 
0.5%
E1 3743
 
0.1%
I 3176
 
0.1%
Other values (7) 4755
 
0.2%

Length

2022-12-10T21:39:40.184475image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wt 1309059
42.3%
b2 1117897
36.1%
wb 282983
 
9.1%
b1 212410
 
6.9%
gmt 89133
 
2.9%
f1 39016
 
1.3%
e2 19383
 
0.6%
cp 14758
 
0.5%
e1 3743
 
0.1%
i 3176
 
0.1%
Other values (7) 4755
 
0.2%

Most occurring characters

ValueCountFrequency (%)
B 1613451
25.7%
W 1592042
25.4%
T 1398192
22.3%
2 1140313
18.2%
1 256720
 
4.1%
M 90649
 
1.4%
G 89283
 
1.4%
F 42000
 
0.7%
E 23126
 
0.4%
P 14779
 
0.2%
Other values (4) 18199
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4881721
77.7%
Decimal Number 1397033
 
22.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B 1613451
33.1%
W 1592042
32.6%
T 1398192
28.6%
M 90649
 
1.9%
G 89283
 
1.8%
F 42000
 
0.9%
E 23126
 
0.5%
P 14779
 
0.3%
C 14768
 
0.3%
I 3410
 
0.1%
Other values (2) 21
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2 1140313
81.6%
1 256720
 
18.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 4881721
77.7%
Common 1397033
 
22.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 1613451
33.1%
W 1592042
32.6%
T 1398192
28.6%
M 90649
 
1.9%
G 89283
 
1.8%
F 42000
 
0.9%
E 23126
 
0.5%
P 14779
 
0.3%
C 14768
 
0.3%
I 3410
 
0.1%
Other values (2) 21
 
< 0.1%
Common
ValueCountFrequency (%)
2 1140313
81.6%
1 256720
 
18.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6278754
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B 1613451
25.7%
W 1592042
25.4%
T 1398192
22.3%
2 1140313
18.2%
1 256720
 
4.1%
M 90649
 
1.4%
G 89283
 
1.4%
F 42000
 
0.7%
E 23126
 
0.4%
P 14779
 
0.2%
Other values (4) 18199
 
0.3%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 MiB
Minimum2022-12-10 21:24:30.135479
Maximum2022-12-10 21:24:30.135479
2022-12-10T21:39:40.415220image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:39:40.595735image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2022-12-10T21:38:33.157228image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:38.009055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:43.225302image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:48.393937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:54.358115image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:59.771807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:05.191656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:12.133789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:27.186479image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:33.592911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:38.458212image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:43.622531image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:48.860926image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:54.772528image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:00.246697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:05.681144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:13.781446image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:27.697592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:34.047802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:38.881437image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:44.043239image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:49.266008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:55.206361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:00.678527image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:06.223606image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:15.458210image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:28.187465image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:34.494531image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:39.297669image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:44.480968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:49.708732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:55.621870image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:01.156719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:06.723009image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:17.315798image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:28.702069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:34.968154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:39.721432image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:44.913909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:50.150861image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:56.070518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:01.635186image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:07.222357image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:18.999844image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:29.213807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:35.481178image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:40.184966image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:45.380629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:50.619117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:56.532151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:02.103097image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:07.728107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:20.703162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:29.892983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:37.116249image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:41.808834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:46.969355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:52.441769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:58.176620image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:03.673479image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:09.412719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:23.077269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:31.566845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:37.699195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:42.361060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:47.561984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:53.286778image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:58.917662image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:04.219020image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:10.049259image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:24.833589image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:32.180759image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:38.132693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:42.783724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:47.983020image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:53.878170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:37:59.330045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:04.668548image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:10.552719image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:26.554340image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-10T21:38:32.718308image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-10T21:39:40.793243image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-10T21:39:41.246157image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-10T21:39:41.528501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-10T21:39:41.816503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-12-10T21:39:42.084322image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-10T21:38:44.203852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-10T21:38:54.829822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-12-10T21:39:23.900172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

cicidi94yri94moni94citi94resi94portarrdatei94modei94addrdepdatei94biri94visacountdtadfilevisapostoccupentdepaentdepdentdepumatflagbiryeardtaddtogenderinsnumairlineadmnumfltnovisatypeupdated_at
04904480.02016.04.0245.0245.0CHI20570.01.0VA20583.048.02.01.020160426BEJNoneGONoneM1968.010252016MNoneAA9.462202e+1000262B22022-12-10 21:24:30.135479
14904481.02016.04.0245.0245.0CHI20570.01.0VA20583.045.02.01.020160426BEJNoneGONoneM1971.010252016FNoneAA9.462196e+1000262B22022-12-10 21:24:30.135479
24904482.02016.04.0245.0245.0CHI20570.01.0VA20583.020.02.01.020160426BEJNoneGONoneM1996.010252016MNoneAA9.462200e+1000262B22022-12-10 21:24:30.135479
34904483.02016.04.0245.0245.0CHI20570.01.0WA20580.039.01.01.020160426BEJNoneGONoneM1977.010252016FNoneAA9.462163e+1000262B12022-12-10 21:24:30.135479
44904490.02016.04.0245.0245.0CHI20570.01.0DE20595.053.02.01.020160426BEJNoneGONoneM1963.010252016FNoneDL9.461481e+1000188B22022-12-10 21:24:30.135479
54904491.02016.04.0245.0245.0CHI20570.01.0LA20645.050.02.01.020160426BEJNoneGONoneM1966.010252016FNoneDL9.461285e+1000188B22022-12-10 21:24:30.135479
64904492.02016.04.0245.0245.0CHI20570.01.0ME20586.050.02.01.020160426BEJNoneGONoneM1966.010252016MNoneDL9.461457e+1000188B22022-12-10 21:24:30.135479
74904493.02016.04.0245.0245.0CHI20570.01.0MINaN59.02.01.020160426BEJNoneGNoneNoneNone1957.010252016FNoneDL9.459601e+1000582B22022-12-10 21:24:30.135479
84904494.02016.04.0245.0245.0CHI20570.01.0MINaN31.02.01.020160426BEJNoneGNoneNoneNone1985.010252016FNoneDL9.459587e+1000582B22022-12-10 21:24:30.135479
94904495.02016.04.0245.0245.0CHI20570.01.0MINaN1.02.01.020160426BEJNoneGNoneNoneNone2015.010252016FNoneDL9.459593e+1000582B22022-12-10 21:24:30.135479
cicidi94yri94moni94citi94resi94portarrdatei94modei94addrdepdatei94biri94visacountdtadfilevisapostoccupentdepaentdepdentdepumatflagbiryeardtaddtogenderinsnumairlineadmnumfltnovisatypeupdated_at
2201504487645.02016.04.0117.0117.0PHI20568.01.0TX20573.037.01.01.020160424NoneNoneGONoneM1979.007222016MNoneAA5.918488e+1000741WB2022-12-10 21:24:30.135479
2201514487646.02016.04.0117.0117.0PHI20568.01.0TX20574.040.02.01.020160424NoneNoneGONoneM1976.007222016FNoneAA5.918871e+1000719WT2022-12-10 21:24:30.135479
2201524487647.02016.04.0117.0117.0PHI20568.01.0TX20574.034.02.01.020160424NoneNoneGONoneM1982.007222016MNoneAA5.918964e+1000719WT2022-12-10 21:24:30.135479
2201534487648.02016.04.0117.0117.0PHI20568.01.0TX20574.034.01.01.020160424NoneNoneGONoneM1982.007222016MNoneAA5.918990e+1000719WB2022-12-10 21:24:30.135479
2201544487649.02016.04.0117.0117.0PHI20568.01.0TX20579.027.01.01.020160424NoneNoneGONoneM1989.007222016MNoneAA5.917996e+1000701WB2022-12-10 21:24:30.135479
2201554487650.02016.04.0117.0117.0PHI20568.01.0US20574.025.01.01.020160424NoneNoneGONoneM1991.007222016FNoneAA5.918947e+1000719WB2022-12-10 21:24:30.135479
2201564487651.02016.04.0117.0117.0PHI20568.01.0VA20574.040.01.01.020160424NoneNoneGONoneM1976.007222016MNoneAA5.918863e+1000719WB2022-12-10 21:24:30.135479
2201574487652.02016.04.0117.0117.0PHO20568.01.0AZ20578.045.02.01.020160424NoneNoneGONoneM1971.007222016MNoneBA5.921783e+1000289WT2022-12-10 21:24:30.135479
2201584487653.02016.04.0117.0117.0PHO20568.01.0AZ20581.035.02.01.020160424NoneNoneGQNoneM1981.007222016MNoneBA5.921791e+1000289WT2022-12-10 21:24:30.135479
2201594487654.02016.04.0117.0117.0PHO20568.01.0AZ20581.032.02.01.020160424NoneNoneGQNoneM1984.007222016MNoneBA5.921792e+1000289WT2022-12-10 21:24:30.135479